AI Data Analyst-Revenue & Growth Analytics - SQL, Claude/LLMs - Remote

Bhavnaditya Accounting And Order Services; United States; À distance; USD 90 000 - 150 000; Plein temps

Résumé du poste

Bhavnaditya Accounting And Order Services is seeking a remote Revenue Analytics Specialist aligned to US Eastern business hours. You will consolidate data from dashboards, spreadsheets, and analytics tools to answer strategic questions for leadership and automate reporting processes.

Lead analysis on channels, trials, conversions, and renewal patterns, evolving the current dashboards into a self-updating Revenue Intelligence Engine. Strong AI aptitude and SQL skills are essential.

Qualifications

• Real AI fluency with daily use of Claude, ChatGPT or similar for analysis, not just drafting emails.
• Strong ability to ask the right questions, structure investigations, and spot data discrepancies.
• Proficient SQL and solid spreadsheet skills for data manipulation and reporting.

Responsabilités

• Tell clear answers to loosely framed business questions with evidence.
• Analyze performance across channels, product lines, and regions to reveal drivers of revenue.
• Build and maintain leadership reporting with automated data refresh and repeatable workflows.

Connaissances

SQL

Data analysis

Spreadsheets

AI fluency

Python

dashboards

Critical thinking

Outils

Claude

ChatGPT

GA4

Amplitude

Mixpanel

Snowflake

BigQuery

Description du poste

Role & Responsibilities:

This is a remote role working US Eastern Standard business hours. You'll work closely with senior revenue leadership at a growing global software company, taking over and then automating the manual work of combining data from dashboards, spreadsheets, web analytics, and product analytics to answer business questions and prepare leadership reporting.

Spot Analysis on Revenue Questions

Take loosely framed business questions and return clear answers with the evidence behind them. Real recent examples: why quarter-to-date B2B revenue growth came almost entirely from renewals, and what that means for new billings; and why trial conversion dropped, traced back to reduced top-of-funnel spend plus a broken targeting change.

Sales Channel and Sales Pattern Analysis

Analyze performance across channels (direct eCommerce, B2B, resellers and partners, paid acquisition), product lines, and regions. Spot seasonality, promotion effects, pricing and discount impact, and changes in the mix between new, renewal, and upgrade revenue.

Installs, Trials, and Conversion

Follow the path from download and install through activation, trial, purchase and renewal. Connect acquisition sources to the in-product moments that predict conversion, so marketing spend can follow what actually converts.

The Revenue Intelligence Engine

Evolve today's dashboard, which runs on static exports, into a self-updating system: automated data refresh, consistent views across teams' spreadsheets, a seasonal/holiday tracker, and eventually the revenue forecast, built as a system rather than a spreadsheet.

A Knowledge Base for the Business

Build and maintain a wiki in Markdown that both people and AI can read: data definitions, notes on each data source, funnel mechanics, and recurring analyses. Knowledge should accumulate rather than get lost.

Leadership Reporting

From about month three, help prepare leadership reports and presentations: check the data behind every claim and add insight, not just charts.

The Data You'll Work With

Today: BI/reporting exports, Google Sheets and Docs, Google Analytics, Search Console, Supermetrics, Amplitude (product events)
Coming soon: Cloud data warehouse exports, additional BI tooling, expanded product analytics

You don't need to know all of these on day one. You need to be able to learn a new data source quickly and spot when it numbers don't add up.

Preferred Candidate Profile:

Real AI fluency. You use Claude, ChatGPT, or similar tools every day for actual analysis work (writing and debugging SQL, analyzing data, building tools and documents), not just drafting emails. This matters more than any BI tool on your CV.

Strong analysis fundamentals. You know which question to ask, how to structure an investigation, and when a number looks wrong.

SQL you're comfortable using on your own, and strong spreadsheet skills.

A systems mindset. You turn one-off analyses into repeatable processes and automated workflows.

Interest in how software businesses make money: acquisition, trials, conversion, renewal, pricing, channels.

Clear written English. Most communication is written and asynchronous, so your written summaries must be easy to act on.

Comfortable working US Eastern Standard business hours

Nice to have:

Experience with SaaS, subscription, or software revenue data. Product analytics (Amplitude, Mixpanel) or web analytics (GA4). Python for data work. Snowflake, BigQuery, or another cloud data warehouse. Experience preparing management reporting.